6 citations · 8 across the 4 of their papers we have counts for
5 papers
SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although aug…
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah +2
Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is disto…
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
Tanvir Hossain, Khaled Mohammed Saifuddin, Muhammad Ifte Khairul Islam +2
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from s…
DyGCL: Dynamic Graph Contrastive Learning For Event Prediction
Muhammed Ifte Khairul Islam, Khaled Mohammed Saifuddin, Tanvir Hossain +1
Predicting events such as political protests, flu epidemics, and criminal activities is crucial to proactively taking necessary measures and implementing required responses to addr…
HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction
Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain +2
Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. C…